Instructions to use eugrug-60/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eugrug-60/lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eugrug-60/lora_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use eugrug-60/lora_model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for eugrug-60/lora_model to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for eugrug-60/lora_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eugrug-60/lora_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="eugrug-60/lora_model", max_seq_length=2048, )
This only example from Unsloth.ai
This model based on Llama 3.2 3B is trained for understand the "Byte Latent Transformer: Patches Scale Better Than Tokens" [research paper]
(https://ai.meta.com/research/publications/## byte-latent-transformer-patches-scale-better-than-tokens/) that was ## published in December 2024.
Uploaded model
- Developed by: eugrug-60
- License: apache-2.0
- Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Inference Providers NEW
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# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eugrug-60/lora_model", dtype="auto")